It’s easy to understand why: data-based decision making is quickly becoming the status quo for successful marketing operations. The only way is to have the right talent in place through sophisticated marketing analytics recruitment and equipping them with the right tools.

But for organizations that don’t already have a robust system for gathering, processing, reading and using Big Data, understanding where to start is an intimidating task.

If you’re not well initiated in the modern application of IT marketing recruitment, understanding the kind of talent you need to take advantage of it poses a challenge. A large enough business will almost certainly need some combination of marketing analytics staffingand data science staffing talent to get the complete picture and wring the most possible ROI from the data it has collected. But understanding the difference between the two, and determining what you need of each, isn’t simple. As marketing analytics recruiters, here’s what we look for when helping our clients find the right talent for their needs:

Fundamental Differences

Data science and analytics are complimentary and require skillsets that overlap significantly. But there are importance nuanced differences that distinguish them. Understanding the roles and responsibilities of each will help you determine which kind of talent and tools you need more of to support your marketing.

At a basic level, marketing analytics operations tend to be more tactical, while marketing data science is somewhat more strategic.

On the other hand, data science is more responsible for understanding how the data you’ve accumulated can have meaningful insights for your company. It is more concerned with anticipating coming trends or forecasting the impact of a particular business decision than looking backwards. Marketing data scientists staffing and executives are often more classically trained in business and management so they can effectively translate your data into actionable advice for the rest of the organization.

Both kinds of expertise are needed to understand the complete story your data has to tell; otherwise you risk getting an incomplete (and probably deceiving) picture.

Additional Nuances Marketing Analytics Recruiters Look For

Marketing Data Analytics

The realm of analytics talent in marketing goes far beyond simple Google Analytics consultants. In general, marketing analytics experts are concerned with what has already happened, and why. This is critical for understanding the ROI of your marketing initiatives, tracking your customers, and attributing conversions to specific points along the buyer’s journey.

To do this they’re often put in charge of gathering the data you need, either through first-party systems (usually preferable) or purchased from a third party. Then they verify the data is trustworthy and “clean” before integrating it into your databases and comparing it with other information.

Marketing analytics is a relatively well-developed discipline, though it continues to evolve on a constant basis. There are certainly far more data analysts than there currently are data scientists. Still, there remains a huge gap marketing analytics skills gap that leaves many organizations with empty seats that desperately need to be filled.

Marketing Data Science

Data science isn’t a brand new discipline, but it’s one that has only started taking off in popularity in recent years. Even so, it has already grown to be one of the most in-demand of all professional jobs, and data scientist recruiters are having a hard time finding enough talent to keep up with demand.

Your data scientist staffing will typically yield experts more interested in predicting the future: upcoming seasonal cycles, unexpected demand spikes, potential competitive threats, etc. They also want to anticipate the effects of your current and planned marketing campaigns: the number of new customers expected from a new commercial, the effects of a new logo, the reaction to a brand announcement, etc.

Data scientists are usually the ones representing the data team in senior management meeting, announcing their findings and visualizing results for the benefit of their less data-savvy peers. They’re also more likely to be involved with advanced data mining techniques and even developing machine learning initiatives.

Though it’s not more important than analytics, data science is certainly more trendy. It was recently named “The Sexiest Job of the 21st Century” by Harvard Business Review, and demand for experts with this job title is spiking.